def compute_datashape(op, operands, kwargs):
dshape = broadcast(*operands)
if isinstance(op, ops.ReductionOp):
if kwargs.get("axis", None):
raise NotImplemented("axis")
dshape = DataShape([coretypes.extract_measure(dshape)])
return dshape
def compute_datashape(op, operands, kwargs):
dshape = broadcast(*operands)
if isinstance(op, ops.ReductionOp):
if kwargs.get("axis", None):
raise NotImplemented("axis")
dshape = DataShape([coretypes.extract_measure(dshape)])
return dshape
def broadcast(*operands):
types = [_get_datashape(op) for op in operands if op is not None]
shapes = []
for t in types:
try:
shapes.append(coretypes.extract_dims(t))
except coretypes.NotNumpyCompatible:
pass
# TODO: broadcasting
datashapes = [coretypes.to_numpy(coretypes.extract_measure(ds)) for ds in types]
type = coretypes.promote_cvals(*datashapes)
if not shapes:
return type
return DataShape(shapes[0] + (type,))
def broadcast(*operands):
types = [_get_datashape(op) for op in operands if op is not None]
shapes = []
for t in types:
try:
shapes.append(coretypes.extract_dims(t))
except coretypes.NotNumpyCompatible:
pass
# TODO: broadcasting
datashapes = [
coretypes.to_numpy(coretypes.extract_measure(ds)) for ds in types
]
type = coretypes.promote_cvals(*datashapes)
if not shapes:
return type
return DataShape(shapes[0] + (type, ))
def test_deconstruct():
ds = dshape('1, 2, 3, int32')
extract_dims(ds) == (1,2,3)
extract_measure(ds) == blaze.int32
def test_deconstruct(self):
ds = dshape('1, 2, 3, int32')
self.assertEqual(extract_dims(ds), (1,2,3))
self.assertEqual(extract_measure(ds), blaze.int32)
def test_deconstruct():
ds = dshape('1, 2, 3, int32')
extract_dims(ds) == (1, 2, 3)
extract_measure(ds) == blaze.int32
def test_deconstruct(self):
ds = dshape('1, 2, 3, int32')
self.assertEqual([int(x) for x in extract_dims(ds)], [1,2,3])
self.assertEqual(extract_measure(ds), blaze.int32)
